A new data-driven modeling method for fermentation processes

Abstract

An accurate model is the premise for successfully implementing fermentation process optimization. Most data-driven models that are widely applied to fermentation processes are unfit for optimization or provide low precision. This paper presents a new data-driven modeling method for directly developing an ANN-based differential model that is fit for optimization. Moreover, this model can provide high precision because it can be discretized using the sampling period of the control variables as the step length. The lack of data pairs is addressed by transforming the model-training problem into a dynamic system parameter identification problem. Further, a particle swarm optimization algorithm with a time-varying escape mechanism (PSOE) is constructed to determine the model parameters. Finally, the uniform design method is used to select the model structure. The results of experiments conducted using practical data for a lab-scale nosiheptide batch fermentation process confirm the effectiveness of the proposed modeling method and PSOE algorithm.